Hello, I'm Shrijith. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product.
AI writes 20% of your code today. Soon, it’ll be 50%, then 75%—and perhaps even more.As AI-generated code proliferates, your engineers and CTO risk losing intellectual control of the codebase.
Everything appears seamless during development: mountains of code are written with AI, shiny demos are built in days instead of months, and teams marvel at AI’s capabilities.
But what happens when this code reaches production? Imagine a 2 AM outage. How will engineers—tasked with keeping applications online—diagnose issues in AI-generated code they barely understand?
The result? Extended downtimes, customer churn, and tangible business harm.
How can forward-looking tech teams counter this *"Code Blindspot Problem"? *
How Black-Box Systems Harm Organizations
- Onboarding: How do engineers quickly ramp up on systems dominated by AI-generated code?
- Institutional Knowledge: How do teams maintain context amid AI’s code volume and velocity?
- Technical Debt: How do organizations address hidden technical debt in AI-generated code?
- Trust: How do companies explain AI-driven systems to customers—or regulators?
- Retention: How do you hold engineers accountable for systems they neither built nor fully comprehend?
When "Control" Becomes Impossible, Build Tools to "Manage"
Traditional programming thrived in a deterministic world—total control, predictable outcomes. AI, however, operates in a non-deterministic realm: the same input can yield different outputs. Sometimes it mimics creativity; other times, it surprises.
Managing AI is less like programming and more like human management. We can infer behavioral patterns but never guarantee exact outcomes. Success lies in leveraging AI’s upside while mitigating risks.
Managing AI Systems Effectively
While AI internals remain opaque, its artifacts (code, logs, inputs) are inspectable. The solution? Better tooling to:
- Slice and dice AI outputs for insights
- Map system behavior at scale
- Demystify production issues
The "set-and-forget" dream is unrealistic. Instead, deploy AI tools to understand AI code:
- AI-assisted code reviews
- AI-driven bug detection/fixes
- AI-powered API/UI analysis
Every development tool—from databases to monitoring—must evolve to operate at higher abstractions.
Hexmos’ Vision: Bridging the Understanding Gap
At Hexmos, we’re tackling this challenge head-on. Our first product, LiveAPI, addresses a critical pain point: as AI-generated and legacy codebases grow, teams lose visibility into their APIs.
How LiveAPI Works:
- Autonomously tracks all code (human/AI-generated)
- Maps every API endpoint, parameter, and dependency
- Provides engineers with discoverability and managers with system-wide visibility
No more Swagger limitations. LiveAPI scales with your codebase, offering real-time insights without manua## The Future of AI-Driven Development
The acceleration is inevitable. To ship faster, personalize deeply, and retain control, teams need tools that:
- Abstract complexity without sacrificing oversight
- Explain AI artifacts at scale
- Evolve alongside AI’s capabilities
Hexmos is committed to this future—where "technical management" replaces "technical control," empowering teams to thrive in an AI-abundant world.
Our metric for success? Helping you ship more, ship faster, and deliver software tailored to every individual—whether your engineers or your customers.
*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
⭐ Star it on GitHub:
HexmosTech
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git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
See It In Action
See git-lrc catch serious security issues such as leaked credentials, expensive cloud operations, and sensitive material in log statements
git-lrc-intro-60s.mp4
Why
- 🤖 AI agents silently break things. Code removed. Logic changed. Edge cases gone. You won't notice until production.
- 🔍 Catch it before it ships. AI-powered inline comments show you exactly what changed and what looks wrong.
- 🔁 Build a habit, ship better code. Regular review → fewer bugs → more robust code → better results in your team.
- 🔗 Why git? Git is universal. Every editor, every IDE, every AI…
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