improvement to act-via-code

Author: codegen-bot

docs/blog/act-via-code.mdx

@@ -10,9 +10,9 @@ description: "The path to advanced code manipulation agents"
 </Frame>
 
 
-Two and a half years since the launch of the GPT-3 API, code assistants have emerged as potentially the premier use case of LLMs. The rapid adoption of AI-powered IDEs and prototype builders isn't surprising — code is structured, deterministic, and rich with patterns, making it an ideal domain for machine learning. Experienced developers working with tools like Cursor (myself included) can tell that the field of software engineering is about to go through rapid change.
+Two and a half years since the launch of the GPT-3 API, code assistants have emerged as potentially the premier use case of LLMs. The rapid adoption of AI-powered IDEs and prototype builders isn't surprising — code is structured, deterministic, and rich with patterns, making it an ideal domain for machine learning. Developers actively working with tools like Cursor (myself included) have an exhiliarating yet ominous sense can tell that the field of software engineering is about to go through rapid change.
 
-Yet there's a striking gap between understanding and action. Today's AI agents can analyze enterprise codebases and propose sophisticated improvements—eliminating tech debt, untangling dependencies, improving modularity. But ask them to actually implement these changes across millions of lines of code, and they hit a wall. Their ceiling isn't intelligence—it's the ability to safely and reliably execute large-scale modifications on real, enterprise codebases.
+Yet there's a striking gap between understanding and action for today's code assistants. When provided proper context, frontier LLMs can analyze massive enterprise codebases and propose practical paths towards sophisticated, large-scale improvements — eliminating tech debt, untangling dependencies, improving modularity. But ask them to actually implement these changes across millions of lines of code, and they hit a wall. 
 
 The bottleneck isn't intelligence — it's tooling. By giving AI models the ability to write and execute code that modifies code, we're about to unlock an entire class of tasks that agents already understand but can't yet perform. Code execution environments represent the most expressive tool we could offer an agent—enabling composition, abstraction, and systematic manipulation of complex systems. When paired with ever-improving language models, this will unlock another step function improvement in AI capabilities.