What is Continuous Optimization?

Continuous Optimization means that all new code being written is continuously optimized for performance, before it is deployed - ensuring it runs at peak performance in production.

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Just as Continuous Integration revolutionized how teams integrate code, and Continuous Deployment changed how we ship software, Continuous Optimization represents the next evolution in the DevOps pipeline. While CI ensures your code builds and runs, CD ensures it deploys reliably, Continuous Optimization ensures optimal performance with every change.

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Through continuous optimization, way we can solve the root cause of slow software.

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Root cause of slow software

Writing optimized code is challenging - it demands both time and expertise from developers. As developers, we're often pressed for time, and not everyone specializes in performance optimization. While developers rightfully prioritize shipping new code and features quickly, they can rarely dedicate enough time to ensure their code runs at peak performance.

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This leads to the situation today where performance is relegated to a monitoring approach. Teams ship potentially slow code to production first, then rely on observability tools to identify performance issues. Engineers must then spend valuable development time fixing these problems after the fact, diverting focus from new features. This approach reduces performance to an afterthought.

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This reactive approach to performance creates a costly cycle: developers build features quickly but must later spend significant time fixing performance issues post-deployment. Technical debt accumulates with each deployment, slowing the team's ability to ship new features. Meanwhile, users suffer through a slow product - damaging both user experience and the company's reputation.

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In performance-critical domains like robotics or financial trading, specialized teams optimize the code written by researchers and algorithm developers since writing high-performance code requires significant time and expertise.

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Ideally, developers would have unlimited time to craft the most performant and well-crafted code, but this isn't realistic.

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Continuous Optimization

So then how can we all ship features quickly while ensuring they run fast? The way is to automate performance optimization during the Pull Request review stage. This is made possible with continuous optimization - where AI agents specialized in performance optimization scan and optimize all developer code. This approach mirrors having specialist performance engineers review and optimize every code change. As a result, we not only prevent performance regressions but also speed up the entire workload with each pull request.

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Codeflash is the first tool that automates continuous optimization

Unlike generic AI code assistants that focus on generating code for new features, Codeflash specializes in understanding performance characteristics. Rather than merely suggesting alternative implementations, it benchmarks them, verifies their correctness through extensive testing, and precisely quantifies performance improvements. Instead of generating vague "AI suggestions," CodeFlash systematically identifies the optimal solution to each performance problem. This focused approach to performance enables Codeflash to discover and validate optimizations that conventional AI coding tools overlook.

Codeflash enables continuous optimization with modern Python codebases. When codeflash is installed on a GitHub repository, it looks at all the new code in a Pull Request, and in the background, automatically does everything that an expert engineer would do to optimize its performance.

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1. It analyzes the intent of the new code.
2. It checks if any existing tests are available for the new code.
3. Generates several new test cases with LLMs.
4. uses advanced techniques like SMT solving to generate new inputs.
5. It finds the right context of the code and generates multiple optimizations with LLMs.
6. It executes the original code to measure the runtime and understand the behavior of the code.
7. Profiles the original code to understand the optimization opportunities.
8. It then applies the optimizations to the codebase and runs the code again.
9. It verifies if the new code has exactly the same behavior as before and if it is faster.
10. It attempts to search for the fastest and the correct optimization.
11. After ensuring the new code meets best practices, it creates a suggestion on the original Pull Request with the optimizations to review and accept. It also mentions how fast would an end to end benchmark get with this optimization.
12. Your workflow just got significantly faster after merging the changes.

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Optimization at the source

Finding the optimization at the Pull Request review stage itself means that all new features being developed are maximally performant and performance regressions are prevented when modifying existing code. Developers are happier since when they see the codeflash optimization, they are confident that the changes are tested for correctness and performance, which they can review and quickly accept. If the new code is already optimal, then codeflash does not find any optimizations and keeps the developer shipping fast.

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Benefits for Every Stakeholder

For developers, Continuous Optimization means freedom to focus on functionality without sacrificing performance. Write your code, and let Codeflash attempt to optimize it, which could make it run 10x faster.

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For engineering managers, it means that all developers are shipping high quality performant code and cause fewer production incidents. No more emergency meetings about slow APIs or bottlenecks.

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For CTOs and technical leaders, it translates to reduced infrastructure costs, improved user experience metrics, and the competitive advantage that comes from consistently fast software. Your engineers can use AI copilots of the world to write code without worrying about AI generated code causing performance problem