NEW Google CodeMender AI Agent is INSANE!

New Google Code Mender AI agent is insane. Google just released an AI that finds bugs in code and fixes them automatically. This thing already submitted 72 security patches to open source projects. Some of these projects have 4. 5 million lines of code. Stay tuned because I'm going to show you exactly what this means and how it works. So, what did Google just announce? Google DeepMind shared early results from Code Mender. This is an AI powered agent that improves code security automatically. It finds vulnerabilities. It creates patches. It validates those patches. Then it submits them for human review. Code mender is an autonomous agent. That means once it's working on a codebase, it can debug and fix complex vulnerabilities. It handles the entire process from finding the root cause to creating and validating patches. Here's why Google built this. Software vulnerabilities are hard to find and fix. Google already has AI tools that find security bugs like big sleep and oss fuzz. These tools discover zeroday vulnerabilities in well- tested software. AI keeps getting better at finding bugs. Humans can't keep up with patching all those bugs. So, Google built Code Mender to solve that gap. Here's what makes Code Mender special. First, it's both reactive and proactive. Reactive means it instantly patches new vulnerabilities when they're discovered. Proactive means it rewrites existing code to eliminate entire classes of vulnerabilities before they can be exploited. It's not waiting for attacks to happen. It's hardening code against future attacks. Second, Code Mender uses Google's Gemini Deepthink models. These are advanced AI models that can reason about complex code. The agent can understand how different parts of a program work together. It can trace bugs back to their root cause. Third, it validates its own work. Codemener doesn't just write a patch and submit it. It checks whether the patch actually fixes the root cause. It makes sure the patch doesn't break anything else. It verifies the patch follows the project's style guidelines. Only highquality patches get sent to humans for review. Let me give you some real examples straight from Google's blog. Example one, a crash report indicated a heat buffer overflow. Most tools would just point to where the crash happened, but Codemen dug deeper. It used debugging tools and code search. It figured out the actual problem was in how the program managed XML elements during passing. The root cause was somewhere else entirely. Uh the final patch only changed a few lines of code, but finding the real problem required serious reasoning. Example two. Code mender worked on a complex object lifetime issue. The agent had to understand the root cause. Then it had to modify a completely custom system for generating C code within the project. That's advanced stuff. Now, let me tell you about my AI profit boardroom. This is the best place to scale your business, get more customers, and save hundreds of hours with AI automation. It currently has more than 1,000 members. These are business owners and entrepreneurs using AI to grow faster and work smarter. If you want to join this community, check the link in the description. Now, let's talk about the proactive side. Google deployed Codemender to work on LIB WEBP. This is a widely used image compression library. Millions of devices use this library. A few years ago, there was a heat buffer overflow vulnerability in libbyp. The vulnerability was CVE 20224863. A threat actor used it in a zeroclick iOS exploit. Code mender applied fbound safety annotations to parts of lib Webb EP. When you add these annotations, the compiler adds bounds checks to the code. This prevents attackers from exploiting buffer overflows. With these annotations in place, that old vulnerability would have been unexploitable. Most other buffer overflows in those sections would be blocked, too. That's the power of proactive security. You're not just fixing known bugs, you're preventing entire categories of bugs from being exploitable. Now, let me explain how Codemener actually works. Code Mender uses advanced program analysis tools. These include static analysis, dynamic analysis, differential testing, fuzzing, and SMT solvers. These tools let Codemender examine code patterns, control flow, and data flow. They help identify the root causes of security flaws. Codemender also uses a multi- aent system. Multiple specialized AI agents work together. One agent might focus on finding the bug. Another agent writes the patch. Another agent critiques validates that the patch doesn't introduce regressions. For example, Codemener has an LLMbased critique tool. This tool compares the original code to the modified code. It highlights the differences. It checks whether the changes introduce any problems. If it finds issues, the main agent can self-correct based on that feedback. This validation process is rigorous. A patch has to fix the root cause of the issue. It has to be functionally correct. It can't cause regressions. It has to follow the project's style guidelines. Only patches that pass all these checks get sent to human researchers for review. And yes, humans are still in the loop. Every patch gets

reviewed by a human before it's submitted to open- source projects. This ensures quality and builds trust with the open-source community. So, what has Codemender accomplished? Over the past 6 months, Codemener has already upstreamed 72 security fixes to open-source projects. Some of these code bases are huge. We're talking 4. 5 million lines of code. Many of these patches have already been accepted by the open- source community. Google is gradually ramping up this process. They're reaching out to maintainers of critical open source projects. They're sharing codemen generated patches. They're gathering feedback. The goal is to eventually release Codemener as a tool that all developers can use. Now, let me tell you why this matters. If you run a business with software, security is critical. But hiring security engineers is expensive. Tools like Code Mender could change that. You could have AI scanning your codebase constantly, finding issues, fixing them, keeping your product secure. If you're a developer, this means you spend less time hunting bugs. You focus on building features. The AI handles the tedious security work. And if you're in the open- source world, this means projects get more secure, even projects that don't have dedicated security teams. All right, what's next for this technology? Google says they'll be publishing technical papers and reports in the coming months. They'll share more details about the techniques and tools they developed. This means other researchers and companies can learn from this work. Google is working closely with the open source community. They're asking for feedback. They're iterating based on what they learn. This collaborative approach ensures codemen actually helps developers and this is just the beginning. Google says they've only started to explore AI's potential for software security. Code mener is one step but there will be more innovations coming. So what does this mean for the bigger picture? We're entering a new phase of software development. AI isn't just helping us write code faster. It's helping us write more secure code. It's finding problems we would have missed. It's fixing those problems automatically. This is going to change how we think about security. Right now, security is often reactive. A bug gets discovered, someone reports it, developers rush to patch it. But Code Mender shows we can be proactive. We can harden code before attacks happen. Now, let me give you some practical takeaways. If you're building software, start thinking about how AI can help with security. There are already tools that can help. Static analysis tools, vulnerability scanners, AI powered code review. Start using these tools now. If you run a business, audit your security processes. Where are the gaps? AI tools like Code Mender can fill some of those gaps. They won't replace human security experts, but they can multiply what those experts can do. If you're learning to code, pay attention to security from day one. Understanding common vulnerabilities is critical. Even if AI tools can find and fix bugs, you still need to know what good security looks like. Now, let me talk about AI agents more broadly. Code Mender is an AI agent, not just an AI tool. What's the difference? A tool waits for you to use it. An agent takes action on its own. Code Mender doesn't wait for you to ask it to find bugs. It actively searches. It makes decisions. It takes steps to solve problems. We're going to see more AI agents in every industry. Agents that handle customer support, agents that manage inventory, agents that optimize marketing campaigns. Understanding this shift is important. Instead of doing every task yourself, you oversee agents. You set goals. You review results. You make strategic decisions. The AI handles execution. All right, let me wrap this up. Google DeepMind released Code Mener. It's an AI agent that finds and fixes security vulnerabilities in code. It uses Gemini Deepthink models to reason about complex bugs. It validates its own patches through multiple checks. It's already submitted 72 security fixes to open source projects in just 6 months. Real examples include fixing a heat buffer overflow in XML passing, creating patches for complex object lifetime issues, and adding bound safety annotations to libweb to prevent buffer overflow exploits. All patches are reviewed by humans before they're submitted. Google is working closely with open-source maintainers. The goal is to release it as a tool for all developers. This technology matters because software security is hard and timeconuming. AI can scale security work in ways humans can't. Now, if you want to learn more about using AI in your business, we have a full SOP and process inside the AI money lab. You get access to over 100 use cases, step-by-step guides, video walkthroughs, plus you join a community with 28,000 members. That's 28,000 people sharing what works with AI. Inside the AI money lab, we give away tutorials every day in the school feed. You get all the video notes. trainings. If you're serious about using AI to make money and grow your business, this is where you need to be. Link in the comments and description. Also, if you want personalized help with SEO and getting more traffic, book a free SEO strategy session with me. I'll show you exactly what you need to do to rank

higher and get more visibility. Link in the comments and description. And don't forget to comment below. Julian Goldie reads every comment. Let me know what you think about Code Mener. What questions do you have? Drop a comment and let's talk. Thanks for watching. I'll see you in the next one.