Software development is evolving rapidly with AI-assisted coding, fast iteration cycles, and complex architectures. Yet, one challenge persists: context balance.
Code reviewers need enough context to grasp changes without being overwhelmed. Too little context leads developers and their AI coding agents to guesswork and errors; too much causes fatigue. While Git tracks line-by-line diffs, it fails to capture deeper functional shifts that shape software behavior. Developers need a way to see not just what changed, but how those changes impact the broader codebase.
To solve this, we built Module Memory—an infrastructure layer that enables both developers and AI assistants to reason about system-level changes. Beyond just a better UI for diffs, Module Memory is a structured, persistent memory layer that makes deviations from standard conventions instantly recognizable.
This core enhancement to Baz's infrastructure enables our code reviewers with:
- A brand new Change Request Graph embedded in every PR
- Smarter CR descriptions, topics, and Baz Reviewer comments
- Clearer visualizations of functional relationships & behavior changes
Get started free at baz.co - read below to learn more about these features in action.
The Change Request Graph: A Visual Representation of Code Evolution
In its initial phase, Module Memory powers the Change Request Graph, a visualization embedded within change requests that represents real functional relationships and behaviors across modules and how modifications impact them.
This dynamic visualization drastically reduces the cognitive load on developers, making code review more effective and with high confidence. It ultimately prevents architectural regressions, especially for teams that are moving fast with AI Code Assistants.
Baz's Change Request Graph extracts real code entities — like functions, classes, and types — and builds accurate, modular-aware graphs that track how behavior, data, and control change across modules. This delivers precise, architecture-aligned insights that go beyond line diffs, highlighting true system changes like behavior shifts or broken integrations. In comparison, Claude and ChatGPT rely on text-based pattern recognition to interpret pull requests, meaning their graphs reflect language cues rather than actual code structure, control flow, or data relationships.
Solving the Context Dilemma: Precision Without Overload
Module Memory introduces a dynamic context window that intelligently surfaces only the most relevant dependencies and relationships. Instead of static, overly broad context dumps, it uses AST (Abstract Syntax Tree) analysis and file sorting to:
- Identify changed files + their callers & calls, so every change is placed in its proper context
- Leverage file sorting capabilities to prioritize only the most relevant dependencies
- Provide a structured, persistent memory layer, allowing both AI and developers to instantly recognize deviations from expected patterns
It provides a smarter, more structured way to handle context, reducing the cognitive burden on developers while improving AI-assisted workflows.
A future roadmap focused on better code comprehension
We’re excited to see how Module Memory transforms the way developers understand and review code. It’s just the beginning of transforming AI Code Review.
Try it today in Baz, and experience a new way to visualize, analyze, and optimize code changes. Get started today.
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