#code-generation

#code-generation

Google announced its widely anticipated Gemini 3 model Tuesday. By many key metrics, it appears to be more capable than the other big generative AI models on the market. In a show of confidence in the performance (and safety) of the new model, Google is making one variant of Gemini-Gemini 3 Pro-available to everyone via the Gemini app starting now. It's also making the same model a part of its core search service for subscribers.

The approach is detailed in a paper authored by MIT's Eagon Meng and Daniel Jackson, titled "What You See is What it Does: A Structural Patten for Legible Software". They flag up the problem of "illegible" modern software, which lacks "direct correspondence between code and observed behavior". Modern software is often, also, "insufficiently modular" they continue, "leading to a failure of three key requirements of robust coding": incrementality, integrity, and transparency.

Vibe coding is a relatively new programming paradigm that emerged with the rise of AI-powered development tools. The term was coined by Andrej Karpathy, a prominent AI researcher and former Director of AI at Tesla, to describe an intuitive way of coding where developers interact with AI models using natural language commands rather than traditional coding syntax. Instead of meticulously writing every line of code, developers simply "vibe" with the AI, describing what they want, and letting the AI generate the necessary code.

Imagine being able to take any live website and instantly transform it into clean, editable code - ready for you to experiment with or build upon. No manual recreations and no starting from scratch. Just paste a link, and watch the site turn into a working codebase in seconds. Sounds like magic, right? But we actually have a solution for this on the market, and it's called Anima Web To Code.

Anthropic claims that Claude Sonnet 4.5 is the best code model in the world. It also comes with significant improvements in reasoning and mathematical skills. On OSWorld, a benchmark for AI models that perform real-world computing tasks, Sonnet 4.5 leads with 61.4 percent. Four months ago, Sonnet 4 scored 42.2 percent on this test. Along with the model, Anthropics is also introducing the Claude Agent SDK.

Its technical performance is particularly notable for its speed. Thanks to prompt caching, grok-code-fast-1 achieves cache hit rates above 90 percent and can handle multiple tool calls before the first output lines are visible. The model supports a wide range of programming languages, including TypeScript, Python, Java, Rust, C++, and Go. It can perform a variety of tasks, from setting up new projects to answering programming questions and targeted bug fixing.

The short answer: AI isn't replacing developers-it's changing what developers do. While AI is undoubtedly transforming the programming landscape, we're witnessing an evolution in how software is built, with AI serving as a powerful collaborator rather than a replacement. Understanding this shift is crucial for anyone concerned about the future of programming jobs. The Current State of AI in Development Today's AI coding tools are impressively capable.

Existing methods for structured commonsense generation typically flatten the output graphs as strings, which often struggles with the generation of well-formed outputs.