 <h1>Congratulations! 🎉 </h1>
    <p style="color:#637381;">All done! You’ve completed another milestone.</p>

To continue, enter your details to log in.

We’ve sent a 6‑digit code to #MAIL_ADDRESS#\nPlease check your inbox or spam folder and enter the code below to continue.

Verification code sent

Thank you for the payment. You can now access {collectionName} by simply clicking the button below.

Yes, you’ve successfully ordered! 🎉

Unlock This Exclusive Course

It seems that your email address is not currently on the authorized access list. Please ensure you are using the email address that was invited, or contact the content owner for assistance.

Verification Required for Access

Diagnosing Antigravity Failures

The Taxonomy of AI Errors

Forensic Analysis of Logs and Diffs

🎮 Pixel Quest: Diagnostics Challenge

Recovery and Isolation Strategies

Strategic Rollbacks and State Management

Surgical Isolation of Defects

Guiding the Agent to Correction

Building Reusable Debugging Patterns

Quiz

Quiz: Failure Classification

Quiz: Log Analysis

Quiz: Rollback Strategies

Quiz: Isolation Techniques

Quiz: Corrective Patterns

Survey

Course Feedback Survey

Antigravity Debugging: Mastering Failure Diagnosis and Recovery is a practical, expert-led course that teaches you how to detect, analyze, and correct failures in AI-assisted development—fast. Whether you’re shipping features with autonomous agents or wrangling complex refactors, this course gives you a battle-tested framework to classify issues, trace root causes, and recover cleanly without losing valuable progress. You’ll learn to distinguish prompt ambiguity from logic errors, spot state corruption, and account for environment constraints that silently derail builds. Through hands-on exercises, you’ll read verbose logs, correlate diffs with execution steps, and separate hallucinations from legitimate syntax or runtime errors. Then, you’ll move beyond diagnosis into controlled recovery: strategic rollbacks, surgical isolation, and safe “dry run” workflows that protect stable code while you guide the agent to precise fixes. Finally, you’ll convert these wins into durable assets—reusable prompts, regression tests, and a personal debugging playbook. Expect actionable techniques, real-world patterns, and a mindset that turns failures into momentum. If you want to level up reliability, ship with confidence, and make your AI agent an ally under pressure, this course is your blueprint.

Course 6: Antigravity Debugging: Mastering Failure Diagnosis and Recovery

<div class="simple-box-wrapper">
    <div class="simple-box">
        <div class="column">
            <p>Building production-level tools with Antigravity requires a fundamental paradigm shift in how developers approach system failures. Unlike traditional software where bugs are often deterministic, AI-driven development introduces a complex taxonomy of errors ranging from prompt ambiguity and logic flaws to state corruption and environmental constraints. Mastering this environment means moving beyond simple trial and error; it demands a forensic understanding of how an agent’s internal thought trace diverges from user intent. This course provides the technical framework necessary to differentiate between legitimate syntax errors and agent hallucinations, ensuring that your production pipeline remains resilient even when the underlying AI logic falters.</p>

<p>Throughout this curriculum, you will develop the skills to execute surgical recovery and isolation strategies that protect the integrity of your codebase. We will explore advanced techniques such as context-aware rollbacks, file-level constraints, and the "Show, Don't Tell" method for guiding agentic correction. By learning to interpret verbose logs and diff outputs, you will gain the ability to pinpoint exactly where reasoning went astray and apply precise fixes without losing project context. Ultimately, the goal is to transform every debugging session into a reusable pattern, creating a personal playbook and regression tests that prevent the recurrence of known failure modes in high-stakes production environments.</p>
        </div>
    </div>
</div>
