We present a comprehensive real-world evaluation of AI-assisted software development tools deployed at enterprise scale. Over one year, 300 engineers across multiple teams integrated an in-house AI platform (DeputyDev) that combines code generation and automated review capabilities into their daily workflows. Through rigorous cohort analysis, our study demonstrates statistically significant productivity improvements, including an overall 31.8% reduction in PR review cycle time. Developer adoption was strong, with 85% satisfaction for code review features and 93% expressing a desire to continue using the platform. Adoption patterns showed systematic scaling from 4% engagement in month 1 to 83% peak usage by month 6, stabilizing at 60% active engagement. Top adopters achieved a 61% increase in code volume pushed to production, contributing to approximately 30 to 40% of code shipped to production through this tool, accounting for an overall 28% increase in code shipment volume. Unlike controlled benchmark evaluations, our longitudinal analysis provides empirical evidence from production environments, revealing both the transformative potential and practical deployment challenges of integrating AI into enterprise software development workflows.

I've always been interested in coding, but I've never had the time to sit down and actually learn how it works. That's why I'm really happy I found this two-part deal that has Microsoft Visual Studio Pro and the Premium Learn to Code Bundle. It gave me some much-needed beginner-friendly coding lessons and the software to actually use what I learned. I do wish I'd waited a few days to buy it though. Then I'd have caught the Early Black Friday Sale and been able to get the whole thing for 55.97.

Traditional AI algorithms, such as Genetic Programming and Reinforcement Learning, often require extensive computational resources to simulate real-world physical scenarios effectively. While advancements in multi-core processing have been made, the inherent limitations of parallelizing rigid body dynamics lead to significant communication overheads, hindering substantial performance gains for simple simulations. This paper introduces NeoPhysIx, a novel 3D physical simulator designed to overcome these challenges. By adopting innovative simulation paradigms and focusing on essential algorithmic elements, NeoPhysIx achieves unprecedented speedups exceeding 1000x compared to real-time. This acceleration is realized through strategic simplifications, including point cloud collision detection, joint angle determination, and friction force estimation. The efficacy of NeoPhysIx is demonstrated through its application in training a legged robot with 18 degrees of freedom and six sensors, controlled by an evolved genetic program. Remarkably, simulating half a year of robot lifetime within a mere 9 hours on a single core of a standard mid-range CPU highlights the significant efficiency gains offered by NeoPhysIx. This breakthrough paves the way for accelerated AI development and training in physically-grounded domains.

We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications. This Visual IDE prioritizes both the Integrity of its development tools and the Visuality of its components, ensuring a smooth and efficient building experience.On one hand, AI2Apps integrates a comprehensive development toolkit ranging from a prototyping canvas and AI-assisted code editor to agent debugger, management system, and deployment tools all within a web-based graphical user interface. On the other hand, AI2Apps visualizes reusable front-end and back-end code as intuitive drag-and-drop components. Furthermore, a plugin system named AI2Apps Extension (AAE) is designed for Extensibility, showcasing how a new plugin with 20 components enables web agent to mimic human-like browsing behavior. Our case study demonstrates substantial efficiency improvements, with AI2Apps reducing token consumption and API calls when debugging a specific sophisticated multimodal agent by approximately 90% and 80%, respectively. The AI2Apps, including an online demo, open-source code, and a screencast video, is now publicly accessible.

Have experience working effectively in globally distributed teams.

Low code development is getting a lot of traction. Low code platform development provides a more accessible (and typically a graphical) interface for developing applications. Low-code development platforms are philosophically similar to the rapid application development tools of the 1990s and early 2000s. Low code development has many benefits, including possible reduction of development time. I believe low-code AI development platforms could be particularly suitable for SMEs.

Hordes of companies are remaking how they do business to stay relevant in a technology-driven world. But hiring great software engineers who can pull off a digital transformation or artificial intelligence project is hard. Enter a growing legion of "citizen developers," empowered by companies like Microsoft, Google, Amazon, Salesforce and ServiceNow. These ordinary business people create apps using software that technology companies call low-code or no-code software development tools. To use these tools, all you need is a web browser.

It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services. The possibilities for AI use grow almost daily, so it's important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions. This can handcuff data scientists and IT as new innovations become available, and results in higher costs than an open environment that supports best-of-breed AI model development and management.

It is no coincidence that companies are investing in AI at unprecedented levels at a time when they are under tremendous pressure to innovate. The artificial intelligence models developed by data scientists give enterprises new insights, enable new and more efficient ways of working, and help identify opportunities to reduce costs and introduce profitable new products and services. The possibilities for AI use grow almost daily, so it's important not to limit innovation. Unfortunately, many organizations do just that by tethering themselves to proprietary tools and solutions. This can handcuff data scientists and IT as new innovations become available, and results in higher costs than an open environment that supports best-of-breed AI model development and management.

Racing car aficionados will probably send me hate mail for this, but my experience has been that there is only one case, in normal driving, where a good automatic transmission is inferior to a manual transmission. Similarly in software development: in almost every case, automatic memory management is superior to manual memory management and results in far greater programmer productivity. If you were developing desktop applications in the early years of Windows, Microsoft offered you two ways to do it: writing C code which calls the Windows API directly and managing your own memory, or using Visual Basic and getting your memory managed for you. These are the two development environments I have used the most, personally, over the last 13 years or so, and I know them inside-out, and my experience has been that Visual Basic is significantly more productive. Often I've written the same code, once in C calling the Windows API and once in Visual Basic, and C always took three or four times as much work.