Comparison
Glue and Potpie.ai both work with AI and codebases, but solve different problems. Glue is for product managers and engineering leaders to understand features, gaps, and dependencies. Potpie powers AI agents to write and execute code for developers.
Choosing the right AI codebase tool depends on who you are and what you're trying to solve. If your team needs to understand why code exists and make better product decisions, that's one problem. If you're trying to automate writing and executing code, that's a different one entirely. Glue and Potpie.ai both work with AI and codebases, but they approach the job from opposite angles.
Potpie.ai is an AI coding agent platform that generates, writes, and executes code changes. It's built for developers who want AI to handle the implementation work - writing functions, fixing bugs, generating boilerplate. Think of it as a more autonomous version of GitHub Copilot.
Glue is codebase intelligence for product managers and engineering leaders. It answers questions about what features exist, where the gaps are, how long changes should take, and what dependencies matter. Think of it as a research tool for people making product decisions and managing technical complexity.
Potpie.ai focuses on the generation problem: how to get AI to write better code faster. They've raised $2.2M in pre-seed funding and are building a platform where AI agents can interpret feature requests and write code in response, changes are generated, tested, and executed automatically, developers specify intent rather than typing implementation, and the system handles iteration.
This is genuinely useful for teams with high-churn feature work or developers who want less typing. Potpie's approach is fundamentally about reducing the work of writing code.
The strength here is clear: if most of your problem is "we code too slowly," AI agents that can write and deploy changes are worth investigating.
Glue solves a different problem: understanding what you have and making smarter decisions about what you build next.
Glue indexes your codebase and uses AI to answer questions in plain English about how features work, what competitive features you're missing, what needs to be modified to add new functionality, and how long features should take based on similar patterns.
The output isn't generated code - it's generated understanding. Product managers get clarity on feature scope before writing a single line. Engineering leaders see dependency chains and architectural patterns. CTOs get a complete picture of technical risk.
Teams using Glue report features shipped 3x faster because planning is tighter, fewer bugs in production because implications are visible upfront, onboarding that takes weeks instead of months, and more accurate estimates grounded in actual code patterns.
This is the critical distinction:
Potpie = Code Generation
Glue = Codebase Understanding
You could use both. A PM could use Glue to research what a feature requires, then hand that spec to a developer who uses Potpie (or Copilot) to implement it. That's actually a pretty logical workflow.
But they're solving different problems. Glue doesn't write code. Potpie doesn't analyze your product strategy or feature gaps.
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